The present embodiments relate to magnetic resonance imaging (MRI). In particular, white matter tractography for brain connectivity analysis with magnetic resonance imaging is provided.
Diffusion tractography is a 3D modeling technique to visually represent neural tracts from diffusion magnetic resonance imaging (MRI) data. Tractography has gained importance in the medical imaging community for the last decade, especially with the initiation of the NIH Blueprint: Human Connectome Project. However, tractography is often used by the neuroscience community while ignoring error, lack of validation or other limitations. While various problems indirectly or directly related to tractography have been addressed (e.g., super-resolved diffusion MR images, better diffusion MRI preprocessing tools, representations that can reduce the errors due to the de facto tensor modeling in crossings and high curvature areas, and fast computation of full brain tractograms), other problems continue to be ignored.
Glasser, et al., in “The Minimal Preprocessing Pipelines for the Human Connectome Project Neuroimage,” provides a benchmark in the research community for processing pipelines for brain connectivity analysis. These pipelines, not only for diffusion MR but also for functional MR datasets, constitute a sequential execution of several methods most of which are publicly available in third party tools. These methods for diffusion tractography may still be considered limited, such as due to processing efficiency. Another approach preprocesses the input diffusion data and finishes execution by aligning diffusion data with available structural MRI data, but does not provide a tractography pipeline. In yet another approach, a MATLAB® toolbox dedicated to diffusion MRI data performs preprocessing, tractography, extraction of diffusion metrics, and construction of brain networks. However, the only diffusion model in this approach is the diffusion tensor. The diffusion tensor may not be accurate in the case of partial volume effects and might jeopardize the reliability of the resulting brain networks.